SYS-CON Events announced today that Technologic Systems Inc., an embedded systems solutions company, will exhibit at SYS-CON's @ThingsExpo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Technologic Systems is an embedded systems company with headquarters in Fountain Hills, Arizona. They have been in business for 32 years, helping more than 8,000 OEM customers and building over a hundred COTS products that have never been discontinued. Technologic Systems' product base consists of a wide variety of off-the-shelf PC/104 single board computers, computer-on-modules, touch panel computers, peripherals and industrial controllers. They also offer custom configurations and design services.

SYS-CON Events announced today that Silicon India has been named "Media Sponsor" of SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Published in Silicon Valley, Silicon India magazine is the premiere platform for CIOs to discuss their innovative enterprise solutions and allows IT vendors to learn about new solutions that can help grow their business. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA.

A robotic drill could be the future of surgery and could help cut the time of a routine brain operation from two hours - to two-and-a-half minutes. Developers believe the computer-driven gadget, which works like'Google Maps', could play a pivotal role in future surgical procedures. It can make one type of complex cranial surgery 50 times faster than standard procedures, researchers say. They claim the drill produces fast, clean and safe cuts, reducing the time the wound is open and the patient is anaesthetised. This decreases the chances of infection, human error and surgical cost.

Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.

Amazon's voice-controlled assistant should soon sound more natural to Brits. The company has upgraded the UK version of Alexa with "Speechcons," an extensive list of words and phrases that can be delivered in a more lively, expressive manner. These include "whoops a daisy," "bob's your uncle" and "oh my giddy aunt," as well as "crikey," "blimey" and "geronimo!" Speechcons are part of the Alexa development platform, meaning anyone can draw on them for third-party "skills." As long as it's wrapped in an "interjection" tag, Alexa will check the word against its Speechcon bank and, if it's listed, enunciate with a little extra oomph.

If there's ever a time you want to spend less time under the knife, it's during brain surgery. Artificial intelligence could help doctors diagnose brain tumors more quickly and more accurately, according to a new study by researchers at the University of Michigan Medical School and Harvard University. "Our goal is to develop an algorithm that approaches the performance of a neuropathologist at diagnosis during an operation," said Dr. Daniel Orringer, first author of the study in Nature Biomedical Engineering and an assistant professor of neurosurgery at Michigan Medicine. In their experiments on more than 100 brain tissue samples, the researchers used deep learning to detect the presence of a tumor and classify it into one of several broad categories. The algorithm analyzes tissue from a laser imaging technique the researchers developed called stimulated Raman histology, or SRH.

When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases. It was then that one of my data science colleagues introduced me to the concept of working out an algorithm in an excel sheet. And that worked wonders for me.

This week Amazon took the wraps off a new incarnation of its Alexa voice assistant, giving the AI an eye so it can see as well as speak and hear. The Echo Look also contains a depth sensor that's being used, in the first instance, to create a bokeh effect for a hands-free style selfies feature that Amazon is hoping will sell the device to fashion lovers, by making their outfits pop out against the bedroom wallpaper, and making them more eager to socially share. The Echo Look app is where users can view the style selfies (and videos) they've asked Alexa to record for them (she indefinitely stores a copy for Amazon too). But the flagship feature of the app is a fashion feedback service, called Style Check, which Amazon says will utilize machine learning to rate fashion choices and help users choose between outfit pairs. And ultimately, presumably, give their entire wardrobe a score.

There is a critically important dialogue going on across the extended global automotive industry about the future evolution of transportation and mobility. This debate is driven by the convergence of a series of industry-changing forces and mega-trends (see figure 1). Innovative technologies are changing how companies develop and build vehicles. Electric and fuel-cell powertrains tend to offer greater propulsion for lower energy investment at lower emission levels.1 New, lightweight materials enable automakers to reduce vehicle weight without sacrificing passenger safety.2 Further breakthroughs are advancing the introduction of autonomous vehicles; increasingly, daily news reports suggest that driverless cars will soon become a commercial reality.3 We have already seen rapid advances in the "connected car"--innovations that integrate communications technologies and the Internet of Things to provide valuable services to drivers.4

Many astounding feats of computer intelligence, from automated language translation to self-driving cars, are based on neural networks: machine learning systems that figure out how to solve problems with minimal human guidance. But that makes the inner workings of neural networks like a black box, opaque even to the engineers who initiate the machine learning process. "If you look at the neural network, there won't be any logical flow that a human can understand, which is very different from traditional software," explains Guy Katz, a postdoctoral research fellow in computer science at Stanford. That opacity can be worrisome when it comes to using neural networks in safety-critical applications, like preventing aircraft collisions. Running hundreds of thousands of successful simulations isn't enough to inspire full confidence, because even one system failure in a million can spell disaster.